Project description:Pseudouridine (Ψ) is an abundant mRNA modification in the mammalian transcriptome, but its function has remained elusive due to the difficulty of transcriptome-wide mapping. We develop nanopore native RNA sequencing for quantitative Ψ analysis that utilizes native content training, machine learning model prediction, and single read coordination. We find interferon inducible Ψ modifications in the interferon stimulated gene transcripts, consistent with a role of Ψ in the efficacy of mRNA vaccines.
Project description:To detect the modifed bases in SINEUP RNA, we compared chemically modified in vitro transcribed (IVT) SINEUP-GFP RNA and in-cell transcribed (ICT) SINEUP RNA from SINEUP-GFP and sense EGFP co-transfected HEK293T/17 cells. Comparative study of Nanopore direct RNA sequencing data from non-modified and modified IVT samples against the data from ICT SINEUP RNA sample revealed modified k-mers positions in SINEUP RNA in the cell.
Project description:Cytosine deaminases have important uses in the detection of epigenetic modifications and in genome editing. However, the range of applications of deaminases is limited by their substrate preference. To expand the toolkit of deaminases, we developed an in-vitro approach that bypasses a major hurdle with their severe toxicity in expression hosts. We screened 175 putative cytosine deaminases, primarily from bacteria, and found enzymes with strong activity on double- and single-stranded DNA in various sequence contexts, including some without any sequence constraints. We also found enzymes that do not deaminate modified cytosines. The remarkable diversity of cytosine deaminases opens new avenues for biotechnological and medical applications. As a demonstration, we developed a single-enzyme methylation sequencing (SEM-seq) method for 5-methylcytosine detection using a novel non-specific, modification-sensitive double-stranded DNA deaminase, MsddA. SEM-seq generated accurate base-resolution maps of 5-methylcytosine in human genome samples including cell free DNA and samples of 10 pg DNA, equivalent to single cell input. This simple and efficient protocol has the potential to allow high-throughput epigenome profiling of scarce biological material.
Project description:Pseudouridine (Ψ) is an abundant mRNA modification in the mammalian transcriptome, but its function has remained elusive due to the difficulty of transcriptome-wide mapping. We develop nanopore native RNA sequencing for quantitative Ψ analysis that utilizes native content training, machine learning model prediction, and single read coordination. We find interferon inducible Ψ modifications in the interferon stimulated gene transcripts, consistent with a role of Ψ in the efficacy of mRNA vaccines.
Project description:State-of-the-art algorithms for m6A detection and quantification via nanopore direct RNA sequencing have been continuously developed, little is known about their capacities and limitations, which makes a comprehensive assessment in urgent need. Therefore, we performed comprehensive benchmarking of 10 computational tools relying on current-based and base-calling “errors” strategies for m6A detection by nanopore sequencing.
Project description:Whole-genome bisulfite sequencing (WGBS) is currently the gold standard for DNA methylation (5-methylcytosine, 5mC) profiling, however the destructive nature of sodium bisulfite results in DNA fragmentation and subsequent biases in sequencing data. Such issues have led to the development of bisulfite-free methods for 5mC detection. Nanopore sequencing is a long read non-destructive approach that directly analyzes DNA and RNA fragments in real time. Recently, computational tools have been developed that enable base-resolution detection of 5mC from Oxford Nanopore sequencing data. In this chapter we provide a detailed protocol for preparation, sequencing, read assembly and analysis of genome-wide 5mC using Nanopore sequencing technologies.
Project description:RNA internal modifications play critical role in development of multicellular organisms and their response to environmental cues. Using nanopore direct RNA sequencing (DRS), we constructed a large in vitro epitranscriptome (IVET) resource from plant cDNA library labeled with m6A, m1A and m5C respectively. Furthermore, after transfer learning, the pre-trained model was used to detect additional RNA internal modification such as m1A, hm5C, m7G and Ψ modification. Finally, we illustrated a global view of epitranscriptome with m6A, m1A, m5C, m7G and Ψ modification in rice seedlings under normal and high salinity environment. In summary, we provided a strategy for creating IVET resource from cDNA library and developed a computational method that use IVET-based transfer learning termed TandemMod for profiling epitranscriptome landscape with co-occupancy of multiple types of RNA modification in plants responsive to environmental signal.